Search results for "multiagent system"

showing 10 items of 108 documents

"Table 25" of "Measurement of event shape and inclusive distributions at s**(1/2) = 130-GeV and 136-GeV."

1997

4-jet rate for the Jade Algorithm.

Computer Science::Multiagent SystemsE+ E- --> 4JET133.0Astrophysics::High Energy Astrophysical PhenomenaE+ E- ScatteringIntegrated Cross SectionExclusiveHigh Energy Physics::ExperimentJet ProductionCross SectionSIGComputer Science::Distributed Parallel and Cluster Computing
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"Table 32" of "Tuning and test of fragmentation models based on identified particles and precision event shape data."

1996

Differential 4-jet rate for the JADE Algorithm. Corrected to final state particles. YCUT is the jet finding cutt-off parameter.

Computer Science::Multiagent SystemsE+ E- --> 4JETDN/DSIGAstrophysics::High Energy Astrophysical PhenomenaE+ E- ScatteringExclusiveHigh Energy Physics::Experiment91.2Single Differential DistributionJet Production
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"Table 26" of "Measurement of event shape and inclusive distributions at s**(1/2) = 130-GeV and 136-GeV."

1997

5-jet rate for the Jade Algorithm.

Computer Science::Multiagent SystemsE+ E- --> 5JET133.0Astrophysics::High Energy Astrophysical PhenomenaE+ E- ScatteringIntegrated Cross SectionExclusiveHigh Energy Physics::ExperimentJet ProductionCross SectionSIGComputer Science::Distributed Parallel and Cluster Computing
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Consensus for switched networks with unknown but bounded disturbances

2006

We consider stationary consensus protocols for networks of dynamic agents with switching topologies. The measure of the neighbors' state is affected by Unknown But Bounded disturbances. Here the main contribution is the formulation and solution of what we call the $\epsilon$-consensus problem, where the states are required to converge in a tube of ray $\epsilon$ asymptotically or in finite time.

Computer Science::Multiagent SystemsOptimization and Control (math.OC)FOS: MathematicsMathematics - Optimization and Control
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A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents

1999

Evaluation of distance or similarity is very important in cooperative problem solving with a group of agents. Distance between problems is used by agents to recognize nearest solved problems for a new problem, distance between solutions is necessary to compare and evaluate the solutions made by different agents, and distance between agents is useful to evaluate weights of the agents to be able to integrate them by weighted voting. The goal of this paper is to develop a similarity evaluation technique to be used for cooperative problem solving with a group of agents. Virtual training environment used for this goal is represented by predicates that define relationships within three sets: prob…

Computer Science::Multiagent SystemsTheoretical computer scienceSimilarity (network science)Computer scienceGroup (mathematics)business.industryStructure (category theory)Weighted votingInformation systemVirtual trainingArtificial intelligencebusinessCooperative problem solving
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Collaborative body sensor networks: Taxonomy and open challenges

2018

International audience; Single Body Sensor Networks (BSNs) have gained a lot of interest during the past few years. However, the need to monitor the activity of many individuals to assess the group status and take action accordingly has created a new research domain called Collaborative Body Sensor Network (CBSN). In such a new field, understanding CBSN's concept and challenges over the roots requires investigation to allow the development of suitable algorithms and protocols. Although there are many research studies in BSN, CBSN is still in its early phases and studies around it are very few. In this paper, we define and taxonomize CBSN, describe its architecture, and discuss its applicati…

Computer science010401 analytical chemistry[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]02 engineering and technology[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation01 natural sciencesData science0104 chemical sciences[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]Open research[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]0202 electrical engineering electronic engineering information engineeringResearch studies[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]020201 artificial intelligence & image processing[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]ArchitectureWireless sensor network2018 IEEE Middle East and North Africa Communications Conference (MENACOMM)
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A self-adaptable distributed CBR version of the EquiVox system

2016

Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enab…

Computer scienceComputation0206 medical engineeringBiomedical EngineeringBiophysicsTherapeutic exposureBioengineeringContext (language use)02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]computer.software_genreMachine learning[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]Voxel0202 electrical engineering electronic engineering information engineeringComputer visionRepresentation (mathematics)Adaptation (computer science)business.industryMulti-agent system020601 biomedical engineering[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Key (cryptography)020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Artificial intelligence[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businesscomputer
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Efficient cluster-based routing algorithm for body sensor networks

2018

International audience; Body Sensor Networks have gained a lot of research interest lately for the variety of applications they can serve. In such networks where nodes might hold critical information about people's lives, designing efficient routing schemes is very important to guarantee data delivery with the lowest delay and energy consumption. Even though some cluster-based routing schemes were proposed in the literature, none of them offer a complete solution that guarantees energy and delay efficient routing in BSN. In this paper, we propose a robust cluster- based algorithm that increases the routing efficiency through every step of the routing process: cluster formation, cluster head…

Computer scienceDistributed computing010401 analytical chemistryRouting algorithm020206 networking & telecommunications[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]02 engineering and technologyEnergy consumption[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation01 natural sciences0104 chemical sciences[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR][INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]0202 electrical engineering electronic engineering information engineering[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Data delivery[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]MATLABcomputerWireless sensor networkCluster basedcomputer.programming_language2018 IEEE Middle East and North Africa Communications Conference (MENACOMM)
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Efficient anomaly detection on sampled data streams with contaminated phase I data

2020

International audience; Control chart algorithms aim to monitor a process over time. This process consists of two phases. Phase I, also called the learning phase, estimates the normal process parameters, then in Phase II, anomalies are detected. However, the learning phase itself can contain contaminated data such as outliers. If left undetected, they can jeopardize the accuracy of the whole chart by affecting the computed parameters, which leads to faulty classifications and defective data analysis results. This problem becomes more severe when the analysis is done on a sample of the data rather than the whole data. To avoid such a situation, Phase I quality must be guaranteed. The purpose…

Computer scienceSample (material)0211 other engineering and technologies02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]01 natural sciences[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing010104 statistics & probabilitysymbols.namesake[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]ChartControl chartEWMA chart0101 mathematics021103 operations researchData stream miningbusiness.industryPattern recognition[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]OutliersymbolsAnomaly detection[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Artificial intelligence[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessGibbs sampling
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Supporting Autonomy in Agent Oriented Methodologies

2016

Designing a software solution for a complex systems is always a demanding task, it becomes much more complex if we consider to design a multi agent system where agents have to exhibit autonomy; which abstractions and which concepts to take into consideration when using a design methodology we would like to support autonomy? In this paper, we answer this question by studying and analyzing literature on the concept of agents in order to establish the basic set of concepts an agent oriented methodology has to deal with.

Computer sciencebusiness.industryManagement sciencemedia_common.quotation_subjectMulti-agent systemComputer Science (all)Complex system020207 software engineering02 engineering and technologyTask (project management)SoftwareDecision Sciences (all)Multiagent systems0202 electrical engineering electronic engineering information engineeringDesign processDesign process020201 artificial intelligence & image processingMultiagent systemDesign procebusinessSet (psychology)AutonomyAutonomymedia_common
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